COPD Classification in CT Images Using a 3D Convolutional Neural Network
This work addresses the need for automated, objective classification of COPD in medical imaging to assist physicians and reduce variability in diagnosis.
The paper tackles the problem of automatically classifying Chronic Obstructive Pulmonary Disease (COPD) and emphysema in CT images, proposing a 3D deep learning approach that uses volume-wise annotations and demonstrates the impact of transfer learning from a pre-trained COPD model.
Chronic obstructive pulmonary disease (COPD) is a lung disease that is not fully reversible and one of the leading causes of morbidity and mortality in the world. Early detection and diagnosis of COPD can increase the survival rate and reduce the risk of COPD progression in patients. Currently, the primary examination tool to diagnose COPD is spirometry. However, computed tomography (CT) is used for detecting symptoms and sub-type classification of COPD. Using different imaging modalities is a difficult and tedious task even for physicians and is subjective to inter-and intra-observer variations. Hence, developing meth-ods that can automatically classify COPD versus healthy patients is of great interest. In this paper, we propose a 3D deep learning approach to classify COPD and emphysema using volume-wise annotations only. We also demonstrate the impact of transfer learning on the classification of emphysema using knowledge transfer from a pre-trained COPD classification model.